In hybrid-imaging systems, two different imaging modalities are used to locate or measure different constituents in a common object space. In one example, two imaging scanners, such as a computed tomography (CT) scanner and a magnetic resonance (MR) scanner, can be used to create images of internal structures (e.g., bones, the spine, etc.) and soft tissue structures (e.g., the brain, vasculature, joints, etc.) within the body. In another example, nuclear scanners, such as positron emission tomography (PET) or single-photon emission computed tomography (SPECT), are coupled with an imaging scanner and can be used to create functional images indicative of metabolic activity and biochemical processes within tissues of the body.
Sometimes, an image from only one modality is available. It may be advantageous to translate the image of the one image modality to emulate an image of another modality. For example, it can be advantageous to translate an MR anatomical image into a CT attenuation image to compare it with an earlier CT image. In another example, MR images can be converted into CT-like attenuation images for attenuation correction in a PET image reconstruction. Another clinical example is generating an image (e.g., a pseudo Fluorodeoxyglucose (FDG)/PET image, a diffusion-weighted whole-body image with background body signal suppression (DWIBS), and the like) from one or more MR images, which could potentially reduce a subject's exposure to radiation without compromising diagnostic confidence. In the domain of computer-aided diagnosis, multi-parametric images can be translated into an underlying pathology of a lesion that could be used to help subject management.
Even though there is an urgent need to establish a correlation across different medical imaging modalities, it is challenging to use conventional or analytic approaches to realize such translation for one or more reasons. First, medical images are prone to noise, which can vary depending on a particular anatomy of a subject and imaging physics. Second, there is typically no 1-to-1 conversion relation among different imaging contrast and modalities. For example, air and bone both have an MR signal that is very close to that of background noise, whereas in CT imaging, air has a near zero attenuation and bone has a very high attenuation. Third, a translation processor that applies to one case of such conversion might not be applicable to a different application owing to different imaging physics of different imaging modalities. Present techniques are subject to ambiguities and errors.
The present application provides new and improved systems and methods which overcome the above-referenced problems and others.
The present disclosure addresses these limitations by providing a generally applicable solution to image contrast conversion based on a machine learning approach. For example, the present disclosure provides systems and methods with a transform processor to resolve the degeneration of imaging contrast translation (i.e., non 1-to-1 mapping) by offering a vector-to-scalar mapping process or a vector-to-vector mapping process. The present disclosure also provides systems and methods with a transform processor that uses input images, exiting images, and/or other basic subject information to train itself and establish a database as prior knowledge. In addition, the present disclosure also uses systems and methods with a machine-learning processor to predict a vector-to-scalar conversion relationship for a target image and/or data generation.